PROJECT SUMMARY/ABSTRACT The overall goal of this work is to understand adaptation in microbial populations, using a combination of mathematical modeling and high-throughput experimental evolution in budding yeast. Specifically, we aim to predict how evolution chooses probabilistically among the spectrum of possible mutational trajectories in these populations. In the short term, evolution depends primarily on the distribution of fitness effects of individual mutations. However, on longer timescales epistatic interactions between mutations can be crucial for adaptation. Similarly, mutations often have different fitness effects in different environments (pleiotropy for fitness). This is essential to long-term adaptation in fluctuating environments. Recent work shows that epistasis and pleiotropy for fitness are strong and common among specific sets of mutations in many microbial and viral systems. However, these studies of specific limited sets of mutations cannot fully explain how epistasis and pleiotropy constrain the rate, repeatability, or dynamics of adaptation in microbial populations. And even given a complete set of epistatic and pleiotropic interactions, we cannot predict how evolution will act in all but a few particularly simple cases. This severely limits our ability to predict th evolution of complex phenotypes, such as compensated antibiotic resistance, multiple mutations required for immune escape, or multiple gene knockouts enabling cancer evolution. The central objective of this proposal is to examine the role of epistasis and pleiotropy for fitness in the evolution of microbial populations. Rather than characterizing specific examples, we propose to survey the overall statistics of epistasis and pleiotropy that are relevant for constraining microbial adaptation. We will then predict how this epistasis and pleiotropy alters how evolution chooses among possible mutational trajectories. In Aim 1, we will measure the statistics of epistasis using a novel strategy for high-throughput experimental evolution. Specifically, we will determine the statistical tendency of different mutational trajectories to diverge in their long-tem prospects. In Aim 2, we will predict how epistasis interacts with genetic variation to constrain th evolution of microbial populations, and test these predictions with laboratory evolution in budding yeast. Finally, in Aim 3, we will measure how the fitness effects of mutations change across related environments and predict how this alters the course of microbial adaptation. We will focus on environmental fluctuations that are particularly common in the evolution of microbial populations, such as adaptation to fluctuating nutrient concentrations and varying intensities of environmental stresses. In contrast to recent work probing epistasis and pleiotropy between small and specific sets of mutations, our approach will provide a comprehensive picture of the degree to which these factors alter the course of microbial evolution.